CutMix.
CutMix is an augmentation strategy incorporating region-level replacement. For a pair of images, patches from one image are randomly cut and pasted onto the other image along with the ground truth labels being mixed together proportionally to the area of patches. Conventional regional dropout strategies [1]}, [2]} have shown evidence of boosting the classification and localization performances to a certain degree, while removed regions are replaced usually with zeros or filled with random noise, which greatly reduce/occlude informative pixels in training images. To this end, instead of simply removing pixels, CutMix replaces the removed regions with a patch from another image, which utilizes the fact that there is no uninformative pixel during training, making it more efficient and effective.
